An emerging and exciting area of clinical science involves the use of data to directly inform the tactics and strategies used to provide treatment in clinical practice. Clinical practice involves the use of dynamic information on patients to individualize treatment, that is, clinical practice requires sequential decision making. To a large extent, the development of methods to inform sequential decision making has occurred outside of statistics (computer science, operations research, engineering). However the developed methods are primarily algorithmic; these methods either do not provide inferential tools, or at best, provide ad hoc inferential tools.

This talk will survey the statistical challenges inherent in using data to inform sequential data making and provide some first solutions.